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International Summit, Smart City 360°

International Summit, Smart City 360°

SmartCity 360 2016, SmartCity 360 2015: Smart City 360° pp 3–15Cite as

A Survey on Mobile Sensing Based Mood-Fatigue Detection for Drivers

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Abstract

The rapid development of the Internet of Things (IoT) has provided innovative solutions to reduce traffic accidents caused by fatigue driving. When drivers are in bad mood or tired, their vigilance level decreases, which may prolong the reaction time to emergency situation and lead to serious accidents. With the help of mobile sensing and mood-fatigue detection, drivers’ mood-fatigue status can be detected while driving, and then appropriate measures can be taken to eliminate the fatigue or negative mood to increase the level of vigilance. This paper presents the basic concepts and current solutions of mood-fatigue detection and some common solutions like mobile sensing and cloud computing techniques. After that, we introduce some emerging platforms which designed to promote safe driving. Finally, we summarize the major challenges in mood-fatigue detection of drivers, and outline the future research directions.

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References

  1. World Health Organization, Global status report on road safety (2009). http://www.who.int/violence_injury_prevention/road_safety_status/2009/en/

  2. Zwaag, M., Dijksterhuis, C., Waard, D., Mulder, B.L.J.M., Westerink, J.H.D.M., Brookhuis, K.A.: The influence of music on mood and performance while driving. Ergonomics 55(1), 12–22 (2012)

    Article  Google Scholar 

  3. Hu, W., Hu, X., Deng, J., et al.: Mood-fatigue analyzer: towards context-aware mobile sensing applications for safe driving. In: Proceedings of the ACM Workshop on Middleware for Context-Aware Applications in the IoT (2014)

    Google Scholar 

  4. Hu, X., Deng, J., Zhao, J., Hu, W., Ngai, E.C.-H., Wang, R., Shen, J., Liang, M., Li, X., Leung, V.C.M., Kwok, Y.: SAfeDJ: a crowd-cloud co-design approach to situation-aware music delivery for drivers. ACM Trans. Multimedia Comput. Commun. Appl. 12(1s), 21 (2015)

    Article  Google Scholar 

  5. Lal, S.K.L., Craig, A.: A critical review of the psychophysiology of driver fatigue. Biol. Psychol. 55(3), 173–194 (2001)

    Article  Google Scholar 

  6. Divjak, M., Bischof, H.: Eye blink based fatigue detection for prevention of computer vision syndrome. In: Proceedings of the Conference on Machine Vision Applications (MVA), pp. 350–353 (2009)

    Google Scholar 

  7. Williamson, A., Chamberlain, T.: Review of on-road driver fatigue monitoring devices (unpublished)

    Google Scholar 

  8. Lin, C.T., Chen, Y.C., Huang, T.Y., Chiu, T.T.: Development of wireless brain computer interface with embedded multitask scheduling and its application on real time driver’s drowsiness detection and warning. IEEE Trans. Biomed. Eng. 55(5), 1582–1591 (2008)

    Article  Google Scholar 

  9. Healey, J., Picard, R.: Smart Car: detecting driver stress. In: Proceedings of the IEEE International Conference on Pattern Recognition, vol. 4, pp. 218–221 (2000)

    Google Scholar 

  10. Kircher, A., Uddman, M., Sandin, J.: Vehicle Control and Drowsiness. Swedish National Road and Transport Research Institute, Linkoping (2002)

    Google Scholar 

  11. Jap, B.T., Lal, S., Fischer, P., Bekiaris, E.: Using EEG spectral components to assess algorithms for detecting fatigue. Expert Syst. Appl. 36(2), 2352–2359 (2009)

    Article  Google Scholar 

  12. Lal, S.K.L., Craig, A., Boord, P., et al.: Development of an algorithm for an EEG based driver fatigue countermeasure. J. Safely Res. 34(3), 321–328 (2003)

    Article  Google Scholar 

  13. Yeo, M.V.M., Li, X.P., Shen, K., et al.: Can SVM be used for automatic EEG detection of drowsiness during car driving. Saf. Sci. 47(1), 115–124 (2009)

    Article  Google Scholar 

  14. Fang, R., Zhao, X., Rong, J., et al.: Study on driving fatigue based on EEG signals. J. Highw. Transp. Res. Dev. 26(S1), 124–126 (2009)

    Google Scholar 

  15. Pate, M., Lala, S.K.L., Kavanagha, D., Rossiterb, P.: Applying neural network analysis on heart rate variability data to assess driver fatigue. Expert Syst. Appl. 38(6), 7235–7242 (2011)

    Article  Google Scholar 

  16. Sharma, N., Banga, V.K.: Development of a drowsiness warning system based on the fuzzy logic. Int. J. Comput. Appl. Technol. 8(9), 1–6 (2010)

    Google Scholar 

  17. Yao, K.P., Lin, W.H., Fang, C.Y., Wang, J.M., Chang, S.L., Chen, S.W.: Real-time vision-based driver drowsiness/fatigue detection system. In: Proceedings of the IEEE Vehicular Technology Conference, pp. 1–5 (2010)

    Google Scholar 

  18. Liu, D., Sun, P., Xiao, Y.Q., Yin, Y.: Drowsiness detection based on eyelid movement. In: Proceedings of the IEEE International Workshop on Education Technology and Computer Science (ETCS), pp. 49–52 (2010)

    Google Scholar 

  19. Tabrizi, P.R., Zoroofi, R.A.: Open/Closed eye analysis for drowsiness detection. In: Proceedings of the Workshops on Image Processing Theory, Tools and Applications, pp. 1–7 (2008)

    Google Scholar 

  20. Berglund, J.: In-Vehicle Prediction of Truck Driver Sleepiness Steering Wheel Variables. Linköpings Universitet, Linköping (2007)

    Google Scholar 

  21. Mattsson, K.: In-Vehicle Prediction of Truck Driver Sleepiness Lane Position Variables. Luleå University of Technology, Södertälje (2007)

    Google Scholar 

  22. Zhao, W., Chellappa, R., Rosenfeld, A., Phillips, P.J.: Face recognition: a literature survey. ACM Comput. Surv. 35(4), 399–458 (2003)

    Article  Google Scholar 

  23. Taheri, S., Turaga, P., Chellappa, R.: Towards view-invariant expression analysis using analytic shape manifolds. In: Proceedings of the IEEE International Conference on Automatic Face and Gesture Recognition & Workshops (FG) (2011)

    Google Scholar 

  24. Tian, Y., Kanade, T., Cohn, J.: Facial expression analysis. In: Handbook of Face Recognition (2005). Chapter 11

    Google Scholar 

  25. Drira, H., Ben Amor, B., et al.: 3D face recognition under expressions, occlusions, and pose variations. Pattern Anal. Mach. Intell. 35(9), 2270–2283 (2013)

    Article  Google Scholar 

  26. Elaiwat, S., Bennamoun, M., et al.: 3-D Face recognition using curvelet local features. Biometrics Compendium 21(2), 172–175 (2014)

    Google Scholar 

  27. Lee, S.H., Plataniotis, K.N., et al.: Intra-class variation reduction using training expression images for sparse representation based facial expression recognition. Affect. Comput. 5(3), 340–351 (2014)

    Article  Google Scholar 

  28. Tie, Y., Cuan, L., et al.: A deformable 3-D facial expression model for dynamic human emotional state recognition. Biometrics Compendium 23(1), 142–157 (2013)

    Google Scholar 

  29. Zheng, W.: Multi-view facial expression recognition based on group sparse reduced-rank regression. Affect. Comput. 5(1), 71–85 (2014)

    Article  Google Scholar 

  30. Qiang, J., Zhu, Z., Lan, P.: Real-time nonintrusive monitoring and prediction of driver fatigue. IEEE Trans. Veh. Technol. 53(4), 1052–1068 (2004)

    Article  Google Scholar 

  31. Edenborough, N., et al.: Driver state monitor from delphi. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), vol. 2, pp. 1206–1207 (2005)

    Google Scholar 

  32. Machine, Seeing. Seeing Machine’s website-FaceLAB (2012)

    Google Scholar 

  33. Hopkins, J.: Microwave and Acoustic Detection of Drowsiness (2005). http://www.jhuapl.edu/ott/technologies/technology/articles/P01471.asp

  34. Samanta, B., Al-Balushi, K.R.: Artificial neural network based fault diagnostics of rolling element bearings using time-domain features. Mech. Syst. Sig. Process. 17(2), 317–328 (2003)

    Article  Google Scholar 

  35. Smart Eye, A. B. Smart Eye Pro (2011)

    Google Scholar 

  36. Ridling, B.L.: Insight and Locus of Control as Related to Aggression in Individuals with Severe Mental Illness SMI (2010)

    Google Scholar 

  37. Applied Science Laboratories product information. Provided on CD-ROM by Virginia Salem, Customer Relations, Applied Science Laboratories (2005)

    Google Scholar 

  38. Hu, X., Li, X., Ngai, E.C.-H., Leung, V.C.M., Kruchten, P.: Multi-dimensional context-aware social network architecture for mobile crowdsensing. IEEE Commun. Mag. 52(6), 78–87 (2014)

    Article  Google Scholar 

  39. Akyildiz, L.F., Su, W., Sankarasubramaniam, Y., Cayirci, E.: A survey on sensor networks. IEEE Commun. Mag. 40(8), 102–114 (2002)

    Article  Google Scholar 

  40. Hu, X., Chu, T.H.S., Leung, V.C.M., Ngai, E.C.-H., Kruchten, P., Chan, H.C.B.: A survey on mobile social networks: applications, platforms, system architectures, and future research directions. IEEE Commun. Surv. Tutorials 17(3), 1557–1581 (2015)

    Article  Google Scholar 

  41. Hamilton, J.: Low cost, low power servers for Internet-scale services. In: Proceedings of Biennial Conference on Innovative Data Systems Research (CIDR) (2009)

    Google Scholar 

  42. Kumar, S., et al.: vManage: loosely coupled platform and virtualization management in data centers. In: Proceedings of the International Conference on Cloud Computing, pp. 127–136 (2009)

    Google Scholar 

  43. Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. J. Internet Serv. Appl. 1(1), 7–18 (2010)

    Article  Google Scholar 

  44. Lee, B., Chung, W.: A smartphone-based driver safety monitoring system using data fusion. Sensors 12(12), 17536–17552 (2012)

    Article  Google Scholar 

  45. Suk, M., Prabhakaran, B.: Real-time mobile facial expression recognition system - a case study. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 132–137 (2014)

    Google Scholar 

  46. Abid, H., Phuong, L., Wang, J., Lee, S., Qaisar, S.: V-Cloud: vehicular cyber-physical systems and cloud computing. In: Proceedings of the ACM International Symposium on Applied Sciences in Biomedical and Communication Technologies (2011). Article 165

    Google Scholar 

  47. Schooley, B., Hilton, B., Lee, Y., McClintock, R., Horan, T.: CrashHelp: a GIS tool for managing emergency medical responses to motor vehicle crashes. In: Proceedings of the Information Systems for Crisis Response and Management (ISCRAM) (2010)

    Google Scholar 

  48. Chan, L., Chong, P.: A lane-level cooperative collision avoidance system based on vehicular sensor networks. In: Proceedings of the ACM International Conference on Mobile Computing and Networking (MobiCom), pp. 131–134 (2013)

    Google Scholar 

  49. Mishra, B., Fernandes, S.L., Abhishek, K., et al.: Facial expression recognition using feature based techniques and model based techniques: a survey. In: Proceedings of the IEEE International Conference on Electronics and Communication Systems (ICECS), pp. 589–594 (2015)

    Google Scholar 

  50. Santos, N., Gummadi, K., Rodrigues, R.: Towards trusted cloud computing. In: Proceedings of the Conference on Hot Topics in Cloud Computing (HotCloud) (2009)

    Google Scholar 

  51. Krautheim, F.J.: Private virtual infrastructure for cloud computing. In: Proceedings of Conference on Hot Topics in Cloud Computing (HotCloud) (2009)

    Google Scholar 

  52. Hu, X., Leung, V.C.M., Li, K., Kong, E., Zhang, H., Surendrakumar, N., TalebiFard, P.: Social drive: a crowdsourcing-based vehicular social networking system for green transportation. In: Proceedings of the ACM MSWiM-DIVANet Symposium, pp. 85–92 (2013)

    Google Scholar 

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Author information

Authors and Affiliations

  1. College of Computer, Wuhan University, Wuhan, China

    Wei Tu

  2. College of Computer Science and Electronic Engineering, Hunan University, Changsha, China

    Lei Wei

  3. School of Computer Science, Carnegie Mellon University, Pittsburgh, USA

    Wenyan Hu

  4. Department of Engineering and Design, University of Sussex, Brighton, UK

    Zhengguo Sheng

  5. Department of Electrical and Computer Engineering, The University of British Columbia, Vancouver, Canada

    Hasen Nicanfar, Xiping Hu & Victor C. M. Leung

  6. Department of Information Technology, Uppsala University, Uppsala, Sweden

    Edith C.-H. Ngai

Authors
  1. Wei Tu
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  2. Lei Wei
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  3. Wenyan Hu
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  4. Zhengguo Sheng
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  5. Hasen Nicanfar
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  6. Xiping Hu
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  7. Edith C.-H. Ngai
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  8. Victor C. M. Leung
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Corresponding author

Correspondence to Wei Tu .

Editor information

Editors and Affiliations

  1. University of Toronto, Toronto, Ontario, Canada

    Alberto Leon-Garcia

  2. Skoda Auto University, Mladá Boleslav, Czech Republic

    Radim Lenort

  3. Skoda Auto University, Mlada Boleslav, Czech Republic

    David Holman

  4. Škoda Auto University, Mladá Boleslav, Czech Republic

    David Staš

  5. Mendel University Brno, Brno, Czech Republic

    Veronika Krutilova

  6. Škoda Auto University, Mladá, Czech Republic

    Pavel Wicher

  7. Slovak University of Technology, Trnava, Slovakia

    Dagmar Cagáňová

  8. Slovak University of Technology, Bratislava, Czech Republic

    Daniela Špirková

  9. Slovak University of Technology, Brno, Czech Republic

    Julius Golej

  10. University of Quebec´s ETS, Montréal, Canada

    Kim Nguyen

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© 2016 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Tu, W. et al. (2016). A Survey on Mobile Sensing Based Mood-Fatigue Detection for Drivers. In: Leon-Garcia, A., et al. Smart City 360°. SmartCity 360 SmartCity 360 2016 2015. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 166. Springer, Cham. https://doi.org/10.1007/978-3-319-33681-7_1

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  • DOI: https://doi.org/10.1007/978-3-319-33681-7_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-33680-0

  • Online ISBN: 978-3-319-33681-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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